Equipment maintenance system and method for heat supply pipe network

Abstract

The invention relates to the technical field of heat supply pipe networks, and discloses an equipment maintenance system and method for a heat supply pipe network, a data acquisition module divides a to-be-monitored heat supply pipe network into a plurality of sub heat supply pipe network monitoring areas to acquire real-time heat supply data; the data analysis module extracts real-time heat supply data of the same type, constructs a real-time heat supply data chain and divides the real-time heat supply data chain into a differential heat supply data chain and a normal heat supply data chain; the first processing module carries out first processing on the normal heat supply data chain and calculates a normal heat supply coefficient; the second processing module carries out second processing on the differential heat supply data chain and calculates a differential heat supply coefficient; the risk overhaul module constructs a risk detection line, calculates a risk detection coefficient, judges whether the to-be-monitored heat supply network needs to be overhauled or not, and determines potential risks through data analysis, so that the overhaul efficiency and accuracy are greatly improved, human errors are reduced, the service life of the heat supply network is prolonged, and the maintenance cost is reduced; wide market application prospects and popularization values are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of heating pipe networks, and in particular to an equipment maintenance system and method for a heating pipe network. Background Art

[0002] The heating network refers to the heating pipelines from the heat source (such as boiler room, direct-fired machine room, heating center, etc.) to the thermal entrance of the building. Multiple heating pipelines form a network for transporting and distributing heating media. The heating network is an important part of the urban centralized heating system. Its design, construction and operation management need to comply with relevant technical standards and specifications to ensure the safety, reliability and economic rationality of the heating system.

[0003] With the acceleration of urbanization, the operation of the heating network, as an important part of urban infrastructure, directly affects the quality of life of residents and the energy security of the city. Therefore, regular inspection and maintenance of the heating network and timely detection and resolution of potential problems are of great significance to ensure the stable operation of the heating system. Traditional methods of heating network maintenance mainly rely on manual inspections and passive repairs after failures. This method is not only time-consuming and labor-intensive, but also difficult to detect risks in time at the early stage of failure, resulting in heating interruption or energy waste. In addition, manual inspections also have certain safety risks. Summary of the invention

[0004] The embodiments of the present invention provide a system and method for equipment maintenance of a heating pipe network, which utilizes data analysis to determine potential risks, greatly improves maintenance efficiency and accuracy, reduces human errors, helps to extend the service life of the heating pipe network, reduces maintenance costs, and has broad market application prospects and promotion value.

[0005] In order to achieve the above object, the present invention provides an equipment maintenance system for a heating pipe network, comprising: A data acquisition module is used to determine the heating network to be monitored, divide the heating network to be monitored into multiple sub-heating network monitoring areas, and acquire real-time heating data of each sub-heating network monitoring area in real time; A data analysis module is used to extract the same type of real-time heating data from the real-time heating data corresponding to each sub-heating pipe network monitoring area, and construct a real-time heating data chain, analyze the real-time heating data chain, and divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result; a first processing module, configured to perform a first processing on the normal heating data chain, and calculate a normal heating coefficient of the normal heating data chain based on a first processing result; A second processing module, used for performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a second processing result; A risk maintenance module is used to construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0006] Furthermore, the data analysis module is used to: The data analysis module is used to obtain a standard heating data range corresponding to each real-time heating data; The data analysis module is used to compare the real-time heating data with the standard heating data range, and if the real-time heating data is within the standard heating data range, the corresponding real-time heating data is divided into the normal heating data chain; The data analysis module is used to divide the corresponding real-time heating data into the difference heating data chain if the real-time heating data is not within the standard heating data range.

[0007] Furthermore, the first processing module is used for: The first processing module is used to calculate the heating data variance of the real-time heating data on the normal heating data chain; The first processing module is used to calculate the normal heating coefficient of the normal heating data link according to the following formula; ; Among them, a1 is the normal heating coefficient of the normal heating data chain, b1 is the number of real-time heating data on the normal heating data chain, and f e is the e-th real-time heating data in the normal heating data chain, and f1 is the variance of the heating data.

[0008] Furthermore, the second processing module is used for: The second processing module is used to extract the same real-time heating data from the differential heating data chain and obtain multiple real-time heating data sets; The second processing module is used to count the number of first real-time heating data in the real-time heating data set; The second processing module is used to extract a real-time heating data from all the real-time heating data sets respectively, and calculate the first real-time heating data set and value; The second processing module is used to obtain preset real-time heating data, eliminate all real-time heating data sets that are smaller than the preset real-time heating data, and count the number of second real-time heating data in the remaining real-time heating data sets; The second processing module is used to extract one real-time heating data from the remaining real-time heating data set, and calculate the second real-time heating data and value; The second processing module is used to calculate the difference heating coefficient of the difference heating data chain according to the first real-time heating data quantity, the second real-time heating data quantity, the first real-time heating data sum value and the second real-time heating data sum value.

[0009] Furthermore, the risk maintenance module is used to: The risk inspection module is used to randomly determine two risk detection nodes, and extract the corresponding first normal heating coefficient, first differential heating coefficient, second normal heating coefficient, and second differential heating coefficient; The risk maintenance module is used to calculate the absolute value of the difference between the first differential heating coefficient and the second differential heating coefficient; The risk maintenance module is used to determine the maximum differential heating coefficient and the minimum differential heating coefficient from all differential heating coefficients, and calculate the differential heating coefficient extreme difference values ​​of the maximum differential heating coefficient and the minimum differential heating coefficient; The risk maintenance module is used to calculate the heating coefficient ratio of the absolute value of the heating coefficient difference and the heating coefficient extreme difference value; The risk maintenance module is used to calculate the absolute value of the normal heating coefficient difference between the first normal heating coefficient and the second normal heating coefficient; The risk maintenance module is used to determine the maximum normal heating coefficient and the minimum normal heating coefficient from all normal heating coefficients, and calculate the normal heating coefficient extreme difference value of the maximum normal heating coefficient and the minimum normal heating coefficient; The risk maintenance module is used to calculate the normal heating coefficient ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient; The risk maintenance module is used to calculate the product value of the difference heating coefficient ratio and the normal heating coefficient ratio, and use it as the sub-risk detection coefficient of the heating network to be monitored; The risk inspection module is used to analyze and calculate all remaining risk detection nodes to determine the sub-risk detection coefficients corresponding to every two risk detection nodes; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all the sub-risk detection coefficients.

[0010] Furthermore, the risk maintenance module is used to: The risk inspection module is used to calculate the coefficient mean of all sub-risk detection coefficients; The risk inspection module is used to divide all sub-risk detection coefficients that are smaller than the coefficient mean into a first sub-risk detection coefficient sequence; The risk inspection module is used to divide all sub-risk detection coefficients greater than or equal to the coefficient mean into a second sub-risk detection coefficient sequence; The risk inspection module is used to calculate the first risk detection coefficient difference between each sub-risk detection coefficient in the first sub-risk detection coefficient sequence and the coefficient mean, and construct a first coefficient difference sequence; The risk inspection module is used to calculate the second risk detection coefficient difference between each sub-risk detection coefficient in the second sub-risk detection coefficient sequence and the coefficient mean, and construct a second coefficient difference sequence; The risk inspection module is used for randomly combining the first coefficient difference sequence and the second coefficient difference sequence in pairs to obtain a plurality of sub-coefficient difference sequences; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences.

[0011] Furthermore, the risk maintenance module is used to: The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored according to the following formula: ; Among them, y is the risk detection coefficient of the heating network to be monitored, n is the number of sub-coefficient difference sequences, g1 i is the first risk detection coefficient difference in the ith sub-coefficient difference sequence, g2 is the second risk detection coefficient difference in the ith sub-coefficient difference sequence, For all The minimum value in For all The maximum value in r 2 For all The variance of .

[0012] Furthermore, the risk maintenance module is used to: The risk maintenance module is used to determine whether the monitored heating pipe network needs maintenance according to the relationship between the risk detection coefficient and the preset risk detection coefficient; The risk maintenance module is used to determine that the heating pipe network to be monitored does not need maintenance when the risk detection coefficient is less than the preset risk detection coefficient; The risk maintenance module is used to determine that the monitored heating pipe network needs maintenance when the risk detection coefficient is greater than or equal to the preset risk detection coefficient.

[0013] In order to achieve the above object, the present invention also provides a method for equipment maintenance of a heating pipe network, characterized in that it comprises: Determine a heating network to be monitored, divide the heating network to be monitored into a plurality of sub-heating network monitoring areas, and obtain real-time heating data of each sub-heating network monitoring area in real time; Extracting the same type of real-time heating data from the real-time heating data corresponding to each sub-heating network monitoring area, and constructing a real-time heating data chain, analyzing the real-time heating data chain, and dividing the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result; Performing a first processing on the normal heating data chain, and calculating a normal heating coefficient of the normal heating data chain based on a result of the first processing; Performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a result of the second processing; A risk detection line for the heating network to be monitored is constructed based on all normal heating coefficients and differential heating coefficients, the risk detection coefficient of the heating network to be monitored is calculated based on the risk detection line, and whether the heating network to be monitored needs maintenance is determined based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses an equipment maintenance system and method for a heating pipe network. A data acquisition module divides a heating pipe network to be monitored into a plurality of sub-heating pipe network monitoring areas to obtain real-time heating data; a data analysis module extracts real-time heating data of the same type, constructs a real-time heating data chain, and divides the real-time heating data chain into a differential heating data chain and a normal heating data chain; a first processing module performs a first processing on the normal heating data chain to calculate a normal heating coefficient; a second processing module performs a second processing on the differential heating data chain to calculate a differential heating coefficient; a risk maintenance module constructs a risk detection line, calculates a risk detection coefficient, determines whether the heating pipe network to be monitored needs maintenance, and uses data analysis to determine potential risks, thereby greatly improving maintenance efficiency and accuracy, reducing human errors, extending the service life of the heating pipe network, and reducing maintenance costs, and having broad market application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of the structure of the equipment maintenance system of the heating pipe network in an embodiment of the present invention is shown; Figure 2 A schematic flow chart of a method for repairing equipment in a heating network according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0016] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0017] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0020] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0021] like Figure 1 As shown, an embodiment of the present invention discloses an equipment maintenance system for a heating pipe network, comprising: A data acquisition module is used to determine the heating network to be monitored, divide the heating network to be monitored into multiple sub-heating network monitoring areas, and acquire real-time heating data of each sub-heating network monitoring area in real time; A data analysis module is used to extract the same type of real-time heating data from the real-time heating data corresponding to each sub-heating pipe network monitoring area, and construct a real-time heating data chain, analyze the real-time heating data chain, and divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result; a first processing module, configured to perform a first processing on the normal heating data chain, and calculate a normal heating coefficient of the normal heating data chain based on a first processing result; A second processing module, used for performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a second processing result; A risk maintenance module is used to construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0022] In this embodiment, the heating pipe network to be monitored is divided into regions according to the area of ​​the heating pipe network to be monitored, so as to ensure the uniformity of the monitoring area of ​​each sub-heating pipe network.

[0023] In this embodiment, the real-time heating data includes heating flow, heating pressure, heating temperature, heating heat, heating vibration, etc.

[0024] In this embodiment, extracting the same type of real-time heating data means extracting the same real-time heating data from each sub-heating network monitoring area, such as extracting the heating flow from each sub-heating network monitoring area to build a real-time heating data chain, extracting the heating pressure from each sub-heating network monitoring area to build a real-time heating data chain, The beneficial effects of the above technical solution are: the present invention uses data analysis to determine potential risks, greatly improves maintenance efficiency and accuracy, reduces human errors, extends the service life of the heating pipeline network, reduces maintenance costs, and has broad market application prospects and promotion value.

[0025] In some embodiments of the present application, the data analysis module is used to: The data analysis module is used to obtain a standard heating data range corresponding to each real-time heating data; The data analysis module is used to compare the real-time heating data with the standard heating data range, and if the real-time heating data is within the standard heating data range, the corresponding real-time heating data is divided into the normal heating data chain; The data analysis module is used to divide the corresponding real-time heating data into the difference heating data chain if the real-time heating data is not within the standard heating data range.

[0026] In this embodiment, each real-time heating data corresponds to a standard heating data range, and the standard heating data range can be set according to actual conditions.

[0027] The beneficial effect of the above technical solution is: the present invention can divide the real-time heating data chain into a differential heating data chain and a normal heating data chain according to the comparison between the real-time heating data and the standard heating data range, laying the foundation for the calculation of the normal heating coefficient and the differential heating coefficient.

[0028] In some embodiments of the present application, the first processing module is used to: The first processing module is used to calculate the heating data variance of the real-time heating data on the normal heating data chain; The first processing module is used to calculate the normal heating coefficient of the normal heating data link according to the following formula; ; Among them, a1 is the normal heating coefficient of the normal heating data chain, b1 is the number of real-time heating data on the normal heating data chain, and f e is the e-th real-time heating data in the normal heating data chain, and f1 is the variance of the heating data.

[0029] The beneficial effect of the above technical solution is: the present invention calculates the normal heating coefficient of the normal heating data chain according to the heating data variance of the real-time heating data on the normal heating data chain, which not only ensures the calculation accuracy of the normal heating coefficient, but also provides reliable data support for risk judgment of the heating pipeline network.

[0030] In some embodiments of the present application, the second processing module is used to: The second processing module is used to extract the same real-time heating data from the differential heating data chain and obtain multiple real-time heating data sets; The second processing module is used to count the number of first real-time heating data in the real-time heating data set; The second processing module is used to extract a real-time heating data from all the real-time heating data sets respectively, and calculate the first real-time heating data set and value; The second processing module is used to obtain preset real-time heating data, eliminate all real-time heating data sets that are smaller than the preset real-time heating data, and count the number of second real-time heating data in the remaining real-time heating data sets; The second processing module is used to extract one real-time heating data from the remaining real-time heating data set, and calculate the second real-time heating data and value; The second processing module is used to calculate the difference heating coefficient of the difference heating data chain according to the first real-time heating data quantity, the second real-time heating data quantity, the first real-time heating data sum value and the second real-time heating data sum value.

[0031] In this embodiment, if the differential heating data link is temperature, the same real-time heating data is {25°C, 25°C}, {22°C, 22°C}, {20°C, 20°C}. The first number of real-time heating data is 3, and one real-time heating data is extracted respectively, that is, 25°C, 22°C and 20°C. The preset real-time heating data is 21°C, and the remaining real-time heating data set is {25°C, 25°C}, {22°C, 22°C}. The second number of real-time heating data is 2, and one real-time heating data is extracted from the remaining real-time heating data set, that is, 25°C and 22°C. The above is shown in terms of distance and is not specifically limited.

[0032] In this embodiment, the differential heating coefficient of the differential heating data link is calculated according to the following formula: ; Among them, u is the differential heating coefficient of the differential heating data chain, w1 is the number of first real-time heating data, w2 is the number of second real-time heating data, q1 is the sum of the first real-time heating data, and q2 is the sum of the second real-time heating data.

[0033] The beneficial effect of the above technical solution is: the present invention calculates the differential heating coefficient of the differential heating data chain according to the number of first real-time heating data, the number of second real-time heating data, the sum of the first real-time heating data and the sum of the second real-time heating data, thereby ensuring the calculation accuracy and convenience of the differential heating coefficient, avoiding calculation errors caused by manual participation, and providing reliable data support for risk judgment of the heating network.

[0034] In some embodiments of the present application, the risk maintenance module is used to: The risk inspection module is used to randomly determine two risk detection nodes, and extract the corresponding first normal heating coefficient, first differential heating coefficient, second normal heating coefficient, and second differential heating coefficient; The risk maintenance module is used to calculate the absolute value of the difference between the first differential heating coefficient and the second differential heating coefficient; The risk maintenance module is used to determine the maximum differential heating coefficient and the minimum differential heating coefficient from all differential heating coefficients, and calculate the differential heating coefficient extreme difference values ​​of the maximum differential heating coefficient and the minimum differential heating coefficient; The risk maintenance module is used to calculate the heating coefficient ratio of the absolute value of the heating coefficient difference and the heating coefficient extreme difference value; The risk maintenance module is used to calculate the absolute value of the normal heating coefficient difference between the first normal heating coefficient and the second normal heating coefficient; The risk maintenance module is used to determine the maximum normal heating coefficient and the minimum normal heating coefficient from all normal heating coefficients, and calculate the normal heating coefficient extreme difference value of the maximum normal heating coefficient and the minimum normal heating coefficient; The risk maintenance module is used to calculate the normal heating coefficient ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient; The risk maintenance module is used to calculate the product value of the difference heating coefficient ratio and the normal heating coefficient ratio, and use it as the sub-risk detection coefficient of the heating network to be monitored; The risk inspection module is used to analyze and calculate all remaining risk detection nodes to determine the sub-risk detection coefficients corresponding to every two risk detection nodes; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all the sub-risk detection coefficients.

[0035] In this embodiment, the difference between the first differential heating coefficient and the second differential heating coefficient is calculated, and then the absolute value is taken to obtain the absolute value of the differential heating coefficient difference.

[0036] In this embodiment, the difference between the maximum differential heating coefficient and the minimum differential heating coefficient is calculated to obtain the differential heating coefficient extreme difference value.

[0037] In this embodiment, the ratio of the absolute value of the heating coefficient difference to the extreme difference of the heating coefficient is calculated to obtain the differential heating coefficient ratio.

[0038] In this embodiment, the difference between the first normal heating coefficient and the second normal heating coefficient is calculated, and then the absolute value is taken to obtain the absolute value of the normal heating coefficient difference.

[0039] In this embodiment, the difference between the maximum normal heating coefficient and the minimum normal heating coefficient is calculated to obtain the normal heating coefficient extreme difference value.

[0040] In this embodiment, the ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient is calculated to obtain the normal heating coefficient ratio.

[0041] In this embodiment, the calculation method of the sub-risk detection coefficients corresponding to the remaining every two risk detection nodes is consistent with the above, and will not be repeated here.

[0042] The beneficial effect of the above technical solution is that the present invention obtains multiple sub-risk detection coefficients by calculation, thereby providing a calculation basis for the risk detection coefficient.

[0043] In some embodiments of the present application, the risk maintenance module is used to: The risk inspection module is used to calculate the coefficient mean of all sub-risk detection coefficients; The risk inspection module is used to divide all sub-risk detection coefficients that are smaller than the coefficient mean into a first sub-risk detection coefficient sequence; The risk inspection module is used to divide all sub-risk detection coefficients greater than or equal to the coefficient mean into a second sub-risk detection coefficient sequence; The risk inspection module is used to calculate the first risk detection coefficient difference between each sub-risk detection coefficient in the first sub-risk detection coefficient sequence and the coefficient mean, and construct a first coefficient difference sequence; The risk inspection module is used to calculate the second risk detection coefficient difference between each sub-risk detection coefficient in the second sub-risk detection coefficient sequence and the coefficient mean, and construct a second coefficient difference sequence; The risk inspection module is used for randomly combining the first coefficient difference sequence and the second coefficient difference sequence in pairs to obtain a plurality of sub-coefficient difference sequences; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences.

[0044] In this embodiment, the first risk detection coefficient difference and the second risk detection coefficient difference are both positive numbers.

[0045] In this embodiment, if there are uncombined first risk detection coefficient differences or second risk detection coefficient differences, the uncombined first risk detection coefficient differences or second risk detection coefficient differences are deleted.

[0046] The beneficial effect of the above technical solution is: the present invention calculates the risk detection coefficient of the heating network to be monitored according to all sub-coefficient difference sequences, provides a basis for judging whether the heating network to be monitored needs maintenance, improves the accuracy and efficiency of judgment, realizes real-time judgment, and avoids the phenomenon of missed judgment.

[0047] In some embodiments of the present application, the risk maintenance module is used to: The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored according to the following formula: ; Among them, y is the risk detection coefficient of the heating network to be monitored, n is the number of sub-coefficient difference sequences, g1 i is the first risk detection coefficient difference in the ith sub-coefficient difference sequence, g2 is the second risk detection coefficient difference in the ith sub-coefficient difference sequence, For all The minimum value in For all The maximum value in r 2 For all The variance of .

[0048] In some embodiments of the present application, the risk maintenance module is used to: The risk maintenance module is used to determine whether the monitored heating pipe network needs maintenance according to the relationship between the risk detection coefficient and the preset risk detection coefficient; The risk maintenance module is used to determine that the heating pipe network to be monitored does not need maintenance when the risk detection coefficient is less than the preset risk detection coefficient; The risk maintenance module is used to determine that the monitored heating pipe network needs maintenance when the risk detection coefficient is greater than or equal to the preset risk detection coefficient.

[0049] In this embodiment, the preset risk detection coefficient is preferably 8, which is used to measure whether the monitored heating network needs to be repaired and processed, and can be set according to actual conditions.

[0050] The beneficial effects of the above technical solution are: the present invention uses data analysis to determine potential risks, greatly improves maintenance efficiency and accuracy, reduces human errors, extends the service life of the heating pipeline network, reduces maintenance costs, and has broad market application prospects and promotion value.

[0051] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0052] Correspondingly, such as Figure 2 As shown, the present application also provides a method for equipment maintenance of a heating pipe network, comprising: S110: determining a heating network to be monitored, dividing the heating network to be monitored into a plurality of sub-heating network monitoring areas, and acquiring real-time heating data of each sub-heating network monitoring area in real time; S120: extracting the same type of real-time heating data from the real-time heating data corresponding to each sub-heating network monitoring area, and constructing a real-time heating data chain, analyzing the real-time heating data chain, and dividing the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result; S130: performing a first processing on the normal heating data chain, and calculating a normal heating coefficient of the normal heating data chain based on the first processing result; S140: performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on the second processing result; S150: Construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0053] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0054] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0055] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A heating network equipment maintenance system, characterized in that: include: A data acquisition module is used to determine the heating network to be monitored, divide the heating network to be monitored into multiple sub-heating network monitoring areas, and acquire real-time heating data of each sub-heating network monitoring area in real time; A data analysis module is used to extract the same type of real-time heating data from the real-time heating data corresponding to each sub-heating pipe network monitoring area, and construct a real-time heating data chain, analyze the real-time heating data chain, and divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result; A first processing module, configured to perform a first processing on the normal heating data chain, and calculate a normal heating coefficient of the normal heating data chain based on a first processing result; A second processing module, used for performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a second processing result; A risk maintenance module is used to construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

2. The equipment maintenance system for the heating pipe network according to claim 1, characterized in that: The data analysis module is used for: The data analysis module is used to obtain a standard heating data range corresponding to each real-time heating data; The data analysis module is used to compare the real-time heating data with the standard heating data range, and if the real-time heating data is within the standard heating data range, the corresponding real-time heating data is divided into the normal heating data chain; The data analysis module is used to divide the corresponding real-time heating data into the difference heating data chain if the real-time heating data is not within the standard heating data range.

3. The equipment maintenance system for the heating pipe network according to claim 1, characterized in that: The first processing module is used for: The first processing module is used to calculate the heating data variance of the real-time heating data on the normal heating data chain; The first processing module is used to calculate the normal heating coefficient of the normal heating data link according to the following formula; ； Among them, a1 is the normal heating coefficient of the normal heating data chain, b1 is the number of real-time heating data on the normal heating data chain, and f e is the e-th real-time heating data in the normal heating data chain, and f1 is the variance of the heating data.

4. The equipment maintenance system for the heating network according to claim 1, characterized in that: The second processing module is used for: The second processing module is used to extract the same real-time heating data from the differential heating data chain and obtain multiple real-time heating data sets; The second processing module is used to count the number of first real-time heating data in the real-time heating data set; The second processing module is used to extract a real-time heating data from all the real-time heating data sets respectively, and calculate the first real-time heating data set and value; The second processing module is used to obtain preset real-time heating data, eliminate all real-time heating data sets that are smaller than the preset real-time heating data, and count the number of second real-time heating data in the remaining real-time heating data sets; The second processing module is used to extract one real-time heating data from the remaining real-time heating data set, and calculate the second real-time heating data and value; The second processing module is used to calculate the difference heating coefficient of the difference heating data chain according to the first real-time heating data quantity, the second real-time heating data quantity, the first real-time heating data sum value and the second real-time heating data sum value.

5. The equipment maintenance system for the heating network according to claim 1, characterized in that: The risk maintenance module is used to: The risk inspection module is used to randomly determine two risk detection nodes, and extract the corresponding first normal heating coefficient, first differential heating coefficient, second normal heating coefficient, and second differential heating coefficient; The risk maintenance module is used to calculate the absolute value of the difference between the first differential heating coefficient and the second differential heating coefficient; The risk maintenance module is used to determine the maximum differential heating coefficient and the minimum differential heating coefficient from all differential heating coefficients, and calculate the differential heating coefficient extreme difference values ​​of the maximum differential heating coefficient and the minimum differential heating coefficient; The risk maintenance module is used to calculate the heating coefficient ratio of the absolute value of the heating coefficient difference and the heating coefficient extreme difference value; The risk maintenance module is used to calculate the absolute value of the normal heating coefficient difference between the first normal heating coefficient and the second normal heating coefficient; The risk maintenance module is used to determine the maximum normal heating coefficient and the minimum normal heating coefficient from all normal heating coefficients, and calculate the normal heating coefficient extreme difference value of the maximum normal heating coefficient and the minimum normal heating coefficient; The risk maintenance module is used to calculate the normal heating coefficient ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient; The risk maintenance module is used to calculate the product value of the difference heating coefficient ratio and the normal heating coefficient ratio, and use it as the sub-risk detection coefficient of the heating network to be monitored; The risk inspection module is used to analyze and calculate all remaining risk detection nodes to determine the sub-risk detection coefficients corresponding to every two risk detection nodes; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all the sub-risk detection coefficients.

6. The equipment maintenance system for the heating network according to claim 5, characterized in that: The risk maintenance module is used to: The risk inspection module is used to calculate the coefficient mean of all sub-risk detection coefficients; The risk inspection module is used to divide all sub-risk detection coefficients that are smaller than the coefficient mean into a first sub-risk detection coefficient sequence; The risk inspection module is used to divide all sub-risk detection coefficients greater than or equal to the coefficient mean into a second sub-risk detection coefficient sequence; The risk inspection module is used to calculate the first risk detection coefficient difference between each sub-risk detection coefficient in the first sub-risk detection coefficient sequence and the coefficient mean, and construct a first coefficient difference sequence; The risk inspection module is used to calculate the second risk detection coefficient difference between each sub-risk detection coefficient in the second sub-risk detection coefficient sequence and the coefficient mean, and construct a second coefficient difference sequence; The risk inspection module is used for randomly combining the first coefficient difference sequence and the second coefficient difference sequence in pairs to obtain a plurality of sub-coefficient difference sequences; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences.

7. The equipment maintenance system for the heating pipe network according to claim 6, characterized in that: The risk maintenance module is used to: The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored according to the following formula: ； Among them, y is the risk detection coefficient of the heating network to be monitored, n is the number of sub-coefficient difference sequences, g1 i is the first risk detection coefficient difference in the ith sub-coefficient difference sequence, g2 is the second risk detection coefficient difference in the ith sub-coefficient difference sequence, For all The minimum value in For all The maximum value in r 2 For all The variance of .

8. The equipment maintenance system for the heating network according to claim 1, characterized in that: The risk maintenance module is used to: The risk maintenance module is used to determine whether the monitored heating pipe network needs maintenance according to the relationship between the risk detection coefficient and the preset risk detection coefficient; The risk maintenance module is used to determine that the heating pipe network to be monitored does not need maintenance when the risk detection coefficient is less than the preset risk detection coefficient; The risk maintenance module is used to determine that the monitored heating pipe network needs maintenance when the risk detection coefficient is greater than or equal to the preset risk detection coefficient.

9. A method for repairing equipment in a heating network, characterized in that: include: Determine a heating network to be monitored, divide the heating network to be monitored into a plurality of sub-heating network monitoring areas, and obtain real-time heating data of each sub-heating network monitoring area in real time; Extracting the same type of real-time heating data from the real-time heating data corresponding to each sub-heating network monitoring area, and constructing a real-time heating data chain, analyzing the real-time heating data chain, and dividing the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result; Performing a first processing on the normal heating data chain, and calculating a normal heating coefficient of the normal heating data chain based on a result of the first processing; Performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a result of the second processing; A risk detection line for the heating network to be monitored is constructed based on all normal heating coefficients and differential heating coefficients, the risk detection coefficient of the heating network to be monitored is calculated based on the risk detection line, and whether the heating network to be monitored needs maintenance is determined based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

Images